The application of Artificial Intelligence (AI) in the transportation sector has brought about a fundamental transformation, enabling improved efficiency, cost reduction, and enhanced sustainability across various systems. This study presents a comprehensive analysis of the impact of AI-powered routing algorithms, demonstrating how these technologies reconfigure transportation frameworks by optimizing resource allocation and minimizing environmental impacts. On the theoretical side, the principles of machine learning, deep learning, and reinforcement learning are explored as the foundation for designing intelligent, adaptive routing systems that dynamically respond to traffic patterns, fuel efficiency, and vehicle performance. Moving from theory to practice, the study evaluates the real-world implications of AI in enhancing logistical operations, fleet management, and urban mobility. These technologies, by reducing fuel consumption, greenhouse gas emissions, and operational costs, prove scalable and applicable across diverse transportation contexts. An economic perspective is also adopted to examine the cost-benefit dynamics of AI implementation, highlighting its role in promoting economic sustainability and supporting low-carbon transportation models. Environmentally, AI-enhanced routing algorithms are presented as effective tools for lowering emissions and advancing long-term ecological goals. Overall, this research identifies AI as a crucial enabler for developing intelligent, efficient, sustainable, and economically viable transportation systems that address both human mobility needs and environmental responsibilities.